A day in daylight

Preface

This document contains the analysis and results for the event A day in daylight, where people from around the world measured a complete day of light exposure on (and around) 22 September 2025.

Importing data

We first set up all packages needed for the analysis

library(LightLogR)
library(Hmisc)

Attaching package: 'Hmisc'

The following objects are masked from 'package:base':

    format.pval, units

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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✔ ggplot2   4.0.0     ✔ tibble    3.3.0
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.1.0     

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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(gt)

Attaching package: 'gt'

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    html

library(gtExtras)
library(gtsummary)
library(legendry)
library(rlang)

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    %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
    flatten_raw, invoke, splice

library(gganimate)

source("https://raw.githubusercontent.com/MeLiDosProject/Data_Metadata_Conventions/main/scripts/overview_plot.R")

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Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUE

Attaching package: 'patchwork'

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Next we import the survey data. Data were collected with REDCap, and there is an import script to load the data in.

source("scripts/prep_survey_data.r")

Connecting light data with survey data

First, we collect a list of available data sets. As we need to compare them to the device ids in the survey, we require only the file without path or extension

path_light <- "data/lightloggers"
files_light <- list.files(path_light) |> tools::file_path_sans_ext()

Next we check which devices are declared in the survey.

survey_devices <- data |> drop_na(device_id) |> pull(device_id) #get devices
survey_devices |> anyDuplicated() #are any entries duplicated?: No

[1] 0

survey_devices |> setequal(files_light) #are light files and survey entries equal?: Yes

[1] TRUE

No entries are duplicated and the survey device Ids are equal to the light files.

Device and location information

Next, we need to get the time zones of the participants and their coordinates. For this, let’s reduce the complexity of the dataset and clean the data

data_devices <- 
data |> 
  drop_na(device_id) |> 
  select(device_id, record_id, 
         city_country, latitude, longitude,
         age, sex = sex.factor,
         complete_log = complete_log.factor,
         behaviour_change = behaviour_change.factor,
         travel_time_zone) |> 
  mutate(travel_time_zone = travel_time_zone == 1)
label(data_devices$travel_time_zone) = "Time zone travel"
label(data_devices$age) = "age"
label(data_devices$behaviour_change) = "Behaviour change"

data_devices |> gt() |> opt_interactive()

Record ID 31 did not finish the post-survey, so we lack data on that device and consequently remove it. Furthermore, Record ID 30 only has data much outside the time frame of interest.

data_devices <- data_devices |> filter(!record_id %in% c("31", "30"))

We also have to clean up the city and country, as well as latitude and longitude data. We do this separately and load the data back in. The manual entries for locations had to be cleaned. This was done with OpenAI through an API key. The results were stored in the file data/cleaned/places.csv. Uncomment the code cell below to recreate the process. Details in outcome may vary, however.

# library(ellmer)
# 
# data_devices_red <- 
# data_devices |> 
#   select(record_id, city_country, latitude, longitude)
# 
# chat <- chat_openai("If there is more then one place specified, only use the first one. If latitude and longitude are misspecified, make a best guess based on city_country. Use IANA names for the time zone identifieres")
# 
# #reducing each line in a table to a single string
# data_devices_red <- 
# data_devices_red |> 
#   pmap(~ paste(paste(names(data_devices_red), c(...), sep = ": "), collapse = ", "))
# 
# #creating an output structure
# type_place <- type_object(
#   record_id = type_string(),
#   city = type_string(),
#   country = type_string(),
#   latitude = type_number(),
#   longitude = type_number(),
#   tz_identifier = type_string(),
#   UTC_dev = type_number("deviation from UTC in hours, given the 22 September 2025")
# )
# 
# places <-
# parallel_chat_structured(
#   chat,
#   data_devices_red,
#   type = type_place
# )
# 
# write.csv(places, "data/cleaned/places.csv")

#read pre-cleaned data in
places <- read_csv("data/cleaned/places.csv")

New names:
Rows: 49 Columns: 8
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(3): city, country, tz_identifier dbl (5): ...1, record_id, latitude,
longitude, UTC_dev
ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
Specify the column types or set `show_col_types = FALSE` to quiet this message.
• `` -> `...1`

places <- 
  places |> dplyr::mutate(record_id = as.character(record_id))

#merge data with main data
data_devices_cleaned <- 
data_devices |> 
  select(-city_country, -latitude, -longitude) |> 
  mutate(record_id = as.character(record_id)) |> 
  left_join(places, by = "record_id") |> 
  mutate(city = case_match(city,
                          "Tuebingen" ~ "Tübingen",
                          "İzmir" ~ "Izmir",
                          .default = city),
         country = case_match(country,
                              "The Netherlands" ~ "Netherlands",
                              c("Turkiye", "Türkiye") ~ "Turkey",
                              c("US", "United States", "USA") ~ 
                                "United States of America",
                              "UK" ~ "United Kingdom",
                              .default = country)
         )

First overview

The following code cells use the data imported so far to create some descriptive plots about the sample.

sex_lab <- primitive_bracket(
  key  = key_range_manual(          # <− positions + labels
    start = c(-7,0.1),
    end = c(-0.1,7),# -6  and  +6  on the x-axis
    name     = c("Males", "Females")
  ),
  position = "bottom"         # draw it at the bottom of the panel
)

data_devices_cleaned |>  
  mutate(
         age_group = 
           cut(age, 
               breaks = seq(15,70,5), 
               labels = c("18-20", "21-25", "26-30", "31-35", 
                          "36-40", "41-45", "46-50", "51-55", 
                          "56-60", "61-65", "66-70"),
               right = TRUE, ordered_result = TRUE),
         ) |> 
  group_by(sex, age_group) |> 
  dplyr::summarize(n = n(), .groups = "drop") |> 
  mutate(n = ifelse(sex == "Male", -n, n)) |> 
  ggplot(aes(x= age_group, y = n, fill = sex)) + 
  geom_col() +
  geom_hline(yintercept = 0) +
  scale_y_continuous(breaks = seq(-6,6, by = 2), 
                     labels = c(6, 4, 2, 0, 2, 4, 6)) +
  scale_fill_manual(values = c(Male = "#2D6D66", Female = "#A23B54")) + 
  guides(fill = "none", alpha = "none",
         x = guide_axis_stack(
           "axis", sex_lab
         )) +
  theme_minimal() +
  coord_flip(ylim = c(-7, 7)) +
  labs(x = "Age (yrs)", y = "n")

ggsave("figures/Fig1_age.pdf", 
       width = 6, height = 6, units = "cm", scale = 1.6)

location_stats <- 
data_devices_cleaned |> 
  dplyr::summarise(
    tz = n_distinct(UTC_dev),
    country = n_distinct(country),
    n = n()
  ) |> 
  pivot_longer(
    cols = everything()
  ) |> 
  dplyr::mutate(name = case_match(name,
                           "country" ~ "Countries",
                           "tz" ~ "Time zones",
                           "n" ~ "Participants"),
         name = factor(name, levels = c("Time zones", "Countries", "Participants"))
         )

P_stats <-
location_stats |> 
  ggplot(aes(y = fct_rev(name), x = value, fill = name)) +
  geom_col() +
  geom_text(aes(label = value), color = "white", hjust = 1.2, fontface = 2, size = 3) +
  theme_minimal() +
  theme_sub_panel(grid = element_blank()) +
  theme_sub_axis_bottom(text = element_blank()) + 
  theme_sub_plot(background = element_rect(fill = alpha("white", 0.75))) +
  labs(x = NULL, y = NULL) +
  scale_fill_manual(values = c(`Time zones` = "deepskyblue3",
                               Participants = "red",
                               Countries = "grey")) +
  guides(fill = "none")

P_tz <-
  data_devices_cleaned |> 
    group_by(UTC_dev) |> 
    dplyr::summarise(n = n()) |> 
    ggplot(aes(x=UTC_dev, y = n)) +
    geom_vline(xintercept = 0, col = "grey") +
    geom_hline(yintercept = 0, col = "grey") +
    geom_col(fill = "deepskyblue3")+
    geom_text(aes(label = n), fontface = 2, vjust = -0.2) +
    theme_minimal() +
    theme_sub_panel(grid.major.y = element_blank(),
                    grid.minor = element_blank()) +
    theme_sub_axis_left(text = element_blank()) +
    scale_x_continuous(breaks = seq(-12, 12, 2)) +
    labs(x = "Deviation from UTC (h) on 22 Sep 2025", y = "n") +
    coord_cartesian(xlim = c(-11,11), ylim = c(NA, 30))

world <- ne_countries(scale = "medium", returnclass = "sf")
    
countries_colors <- tibble(
      country = data_devices_cleaned |> dplyr::count(country) |> pull(country),
      color   = "#0073C2FF",
      stringsAsFactors = FALSE
    )
    
world$color <- ifelse(
      world$name %in% countries_colors$country,
      countries_colors$country[match(world$name, countries_colors$country)],
      NA
    )
    
location_info <- tibble(
      country  = data_devices_cleaned |> pull(country),
      lat      = data_devices_cleaned |> pull(latitude),
      lon      = data_devices_cleaned |> pull(longitude),
      color    = "#0073C2FF",
      stringsAsFactors = FALSE
    ) |> 
  mutate(lat2 = plyr::round_any(lat, 12), 
         lon2 = plyr::round_any(lon, 12)) |> 
  dplyr::summarize(
    .by = c(lat2, lon2),
    lat = mean(lat),
    lon = mean(lon),
    color = unique(color),
    n = n()
  )
    
locations <- st_as_sf(location_info, coords = c("lon", "lat"), crs = 4326)
world_proj     <- st_transform(world,    crs = "+proj=eqc")
locations_proj <- st_transform(locations, crs = "+proj=eqc")
bb <- st_bbox(world_proj)
tz <- sf::st_read("data/tz_now/combined-shapefile-with-oceans-now.shp")  # or .gpkg / .geojson

Reading layer `combined-shapefile-with-oceans-now' from data source 
  `/Users/zauner/Documents/Arbeit/12-TUM/2025_ADayInDaylight_Data/data/tz_now/combined-shapefile-with-oceans-now.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 92 features and 1 field
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -90 xmax: 180 ymax: 90
Geodetic CRS:  WGS 84

tz_lines <- sf::st_boundary(tz)

P_map <-
  ggplot() +
  geom_sf(
    data = world_proj,
    # aes(fill = color),
    fill = "grey",
    color = NA,
    size = 0.25,
    alpha = 0.5,
    show.legend = FALSE
  ) +
  geom_sf(data = tz_lines,
          colour = "deepskyblue3",
          linewidth = 0.15) +
  geom_sf(
    data = locations_proj,
    aes(size = n),
    fill = "red",
    alpha = 0.9,
    shape = 21,
    color = "#0073C2FF",
    stroke = 0.2
  ) +
  geom_sf_text(
    data = locations_proj,
    aes(label = n),
    size = 1.5,
    fontface = 2,
    color = "white",
    alpha = 0.75
  ) +
  scale_fill_manual(values = rep("#0073C2FF", 15)) +
  scale_size_continuous(range = c(2, 5)) +
  theme_minimal() +
  theme(legend.position = "none") +
  labs(x = NULL, y = NULL) +
  coord_sf(expand = FALSE)

(P_map + inset_element(P_stats, 0.05, 0.05, 0.25, 0.25)) / P_tz + plot_layout(heights = c(4.4,1))

ggsave("figures/Fig2_location.pdf",P_map / P_tz + plot_layout(heights = c(4.5,1)),
       width = 15, height = 10, units = "cm", scale = 1.6)
ggsave("figures/Fig2_location.png",
       (P_map + inset_element(P_stats, 0.03, 0.03, 0.28, 0.28)) / P_tz + plot_layout(heights = c(4.4,1)),
       width = 14, height = 9.5, units = "cm", scale = 1.6)

Import wearable data

Next, we import the light data. There are two devices in use: ActLumus and ActTrust we need to import them separately, as they are using different import functions. device_id with four number indicate the ActLumus devices, whereas with seven numbers the ActTrust. We add a column to the data indicating the Type of device in use. We also make sure that the spelling equals the supported_devices() list from LightLogR. Then we construct filename paths for all files.

c("ActLumus", "ActTrust") %in% supported_devices()

[1] TRUE TRUE

data_devices_cleaned <- 
data_devices_cleaned |> 
  dplyr::mutate(device_type = 
           case_when(
              str_length(device_id) == 4 ~ "ActLumus",
              str_length(device_id) == 7 ~ "ActTrust"
           ),
         file_path = glue("data/lightloggers/{device_id}.txt")
         )

data_devices_cleaned <- 
data_devices_cleaned |> 
  dplyr::mutate(
    data = purrr::pmap(list(x = device_type, y = file_path, z = tz_identifier), 
                       \(x, y, z) {
                         import_Dataset(device = x, filename = y, tz = z,
                                        silent = TRUE)
                       }
    )
  )

We end with one dataset per row entry. As the two ActTrust files do not contain a melanopic EDI column, we will use the photopic illuminance column LIGHT towards that end. As there are only two participants with this shortcoming, it will not influence results overduly.

data_devices_cleaned <- 
data_devices_cleaned |> 
  dplyr::mutate(
    data = purrr::map2(device_type, data, 
                       \(x,y) {
                         if(x == "ActTrust") {
                           y |> dplyr::rename(MEDI = LIGHT)
                         }
                         else y
                         }
                       )
  )

Further, the dataset in Malaysia had a device malfunction on 22 September and only worked from the 23 September onwards. As there are minimal differences between dates and very few datasets in that region, we will not dismiss that dataset but rather shift data by one day.

data_devices_cleaned <- 
data_devices_cleaned |> 
  dplyr::mutate(
    data = purrr::map2(record_id, data, 
                       \(x,y) {
                         if(x == "25") {
                           y |> dplyr::mutate(Datetime = Datetime - ddays(1))
                         }
                         else y
                         }
                       )
  )

Lastly, Record ID 44 has a similar issue, yet in the other direction as to Malaysia. Thus we will shift that dataset forward by 2 days.

data_devices_cleaned <- 
data_devices_cleaned |> 
  dplyr::mutate(
    data = purrr::map2(record_id, data, 
                       \(x,y) {
                         if(x == "44") {
                           y |> dplyr::mutate(Datetime = Datetime + ddays(2))
                         }
                         else y
                         }
                       )
  )

Light data

Cleaning light data

In this section we will prepare the light data through the following steps:

• resampling data to 5 minute intervals
• filling in missing data with explicit gaps
• removing data that does not fall between 2025-09-21 10:00:00 UTC and 2025-09-23 12:00:00 UTC, which contains all times where 22 September occurs somewhere on the planet

data_devices <-
data_devices_cleaned |> 
  dplyr::mutate(
    data = purrr::map(data, 
                       \(x) {
                           x |> 
                           aggregate_Datetime("5 mins") |> #resample to 5 mins
                           gap_handler(full.days = TRUE) |> #put in explicit gaps
                           filter_Datetime(start = "2025-09-21 10:00:00",
                                           end = "2025-09-23 12:00:00",
                                           tz = "UTC") #cut out a section of data
                         }
                       )
  )

Next, we add a secondary Datetime column that runs on UTC time.

data_devices <-
data_devices |> 
  dplyr::mutate(
    data = purrr::map(data, 
                       \(x) {
                           x |> 
                           dplyr::mutate(Datetime_UTC = Datetime |> force_tz("UTC"))
                         }
                       )
  )

Visualizing light data

Now we can visualize the whole dataset - first by combining all datasets.

start_dt <- as.POSIXct("2025-09-21 10:00:00", tz = "UTC")
start_dt2 <- as.POSIXct("2025-09-22 00:00:00", tz = "UTC")
end_dt <- as.POSIXct("2025-09-23 12:00:00", tz = "UTC")
end_dt2 <- as.POSIXct("2025-09-23 00:00:00", tz = "UTC")
light_data <- 
  join_datasets(!!!data_devices$data) |> 
  mutate(Datetime = Datetime |> with_tz("UTC"))

light_data |> 
  aggregate_Datetime("1hour") |> 
  gg_days(facetting = FALSE, 
          group = Id, 
          geom = "ribbon",
          lwd = 0.25,
          fill = "skyblue3",
          color = "skyblue4",
          alpha = 0.1,
          y.axis.label = "UTC Time"
          ) +
  geom_vline(xintercept = c(start_dt, end_dt), color = "red")

light_data |> 
  aggregate_Datetime("1hour") |> 
  gg_days(Datetime_UTC,
          geom = "ribbon",
          facetting = FALSE,
          fill = "skyblue3",
          color = "skyblue4",
          alpha = 0.1,
          group = Id, 
          lwd = 0.25,
          y.axis.label = "Local Time"
          ) +
  geom_vline(xintercept = c(start_dt2, end_dt2), color = "red")

light_data |> 
  aggregate_Datetime("1hour") |> 
  gg_days(Datetime_UTC,
          facetting = FALSE, 
          group = Id, 
          lwd = 0.25,
          y.axis.label = "UTC Time"
          ) +
  geom_vline(xintercept = c(start_dt2, end_dt2), color = "red")

boundaries <- tibble(
  start = c(start_dt, start_dt2),
  end = c(end_dt, end_dt2),
  name = c("UTC Time", "Local Time")
)

p <- 
light_data |> 
  aggregate_Datetime("2 hours") |>
  select(Id, Datetime, Datetime_UTC, MEDI) |> 
  pivot_longer(-c(Id, MEDI)) |> 
  mutate(name = case_match(name,
                           "Datetime" ~ "UTC Time",
                           "Datetime_UTC" ~ "Local Time")) |> 
  dplyr::mutate(name = factor(name)) |> 
    gg_days(value,
            geom = "ribbon",
            fill = "skyblue3",
            alpha = 0.4,
            color = "black",
          facetting = FALSE, 
          group = Id, 
          lwd = 0.1,
          x.axis.label = "{next_state} {if(transitioning) '(transitioning)' else ''}",
          y.axis.label = "Melanopic EDI (lx)",
          x.axis.breaks = \(x) Datetime_breaks(x, by = "6 hours", shift = 0),
          x.axis.format = "%H:%M"
    )  + 
  geom_vline(data = boundaries, aes(xintercept=start), col = "red", lty = 2, inherit.aes = FALSE)+
  geom_vline(data = boundaries, aes(xintercept=end), col = "red", lty = 2, inherit.aes = FALSE)+
  geom_segment(data = boundaries, 
               aes(y = 25000, x = start, xend = end), 
               arrow = arrow(length = unit(0.1, "inches"), ends = "both"), col = "red",  
               inherit.aes = FALSE)+
  annotate(geom = "text", y = 25000, x = mean(c(start_dt2, end_dt2)), 
           vjust = -0.4, label = "Global 22 September", col = "red") +
  transition_states(
    name, 
    transition_length = 1,
    state_length = 1
  )

if(interactive()){
animation <- 
animate(p, width = 1200, height = 700, res = 150)

animation

anim_save("figures/patterns.gif", animation)
}

Events

Cleaning events

In this section we deal with with the activity logs - first by filtering them out of the dataset, and selecting the relevant aspects.

events <- 
data |> 
  filter(redcap_repeat_instrument == "log_a_new_activity") |> 
  select(record_id,
         type.factor, 
         social_context.factor,
         wear_activity.factor,
         nonwear_activity.factor,
         nighttime.factor,
         setting_level01.factor, 
         setting_level02_indoors.factor,
         setting_level02_indoors_home.factor,
         setting_level02_indoors_workingspace.factor,
         setting_level02_indoors_healthfacility.factor,
         setting_level02_indoors_learningfacility.factor,
         setting_level02_indoors_leisurespace.factor,
         setting_level02_indoors_retailfacility.factor,
         setting_level02_mixed.factor,
         setting_level02_outdoors.factor,
         lighting_scenario_daylight___1.factor,
         lighting_scenario_daylight___2.factor,
         lighting_scenario_daylight___3.factor,
         lighting_scenario_daylight___4.factor,
         lighting_scenario_3___1.factor,
         lighting_scenario_3___2.factor,
         lighting_scenario_3___3.factor,
         lighting_scenario_2___1.factor,
         lighting_scenario_2___2.factor,
         lighting_scenario_2___3.factor,
         lighting_scenario_2___4.factor,
         autonomy.factor,
         notes, 
         startdate, enddate
         )

#adding labels to the factors
label(events$type.factor) = "Wear type: Are you wearing the light logger at the moment?"
label(events$social_context.factor) = "Are you alone or with others?"
label(events$wear_activity.factor) = "Wear activity "
label(events$nonwear_activity.factor) = "Non-wear activity"
label(events$nighttime.factor) = "Where was the light logger when you were asleep?"
label(events$setting_level01.factor) = "Select the setting"
label(events$setting_level02_indoors.factor) = "Indoors setting"
label(events$setting_level02_indoors_home.factor) = "Indoors setting (home)"
label(events$setting_level02_indoors_workingspace.factor) = "Indoors setting (working space)"
label(events$setting_level02_indoors_learningfacility.factor) = "Indoors setting (learning facility)"
label(events$setting_level02_indoors_retailfacility.factor) = "Indoors setting (retail facility)"
label(events$setting_level02_indoors_healthfacility.factor) = "Indoors setting (health facility)"
label(events$setting_level02_indoors_leisurespace.factor) = "Indoors setting (leisure space)"
label(events$setting_level02_outdoors.factor) = "Outdoors setting"
label(events$setting_level02_mixed.factor) = "Indoors-outdoors setting"
label(events$lighting_scenario_daylight___1.factor) = "Select lighting setting (daylight) (choice=Outdoors (direct sunlight))"
label(events$lighting_scenario_daylight___2.factor) = "Select lighting setting (daylight) (choice=Outdoors (in shade / cloudy))"
label(events$lighting_scenario_daylight___3.factor) = "Select lighting setting (daylight) (choice=Indoors (near window / exposed to daylight))"
label(events$lighting_scenario_daylight___4.factor) = "Select lighting setting (daylight) (choice=Indoors (away from window))"
label(events$lighting_scenario_3___1.factor) = "Select lighting setting (electric light) (choice=Lights are switched on)"
label(events$lighting_scenario_3___2.factor) = "Select lighting setting (electric light) (choice=Low-light or dimmed lights)"
label(events$lighting_scenario_3___3.factor) = "Select lighting setting (electric light) (choice=Completed darkness)"
label(events$lighting_scenario_2___1.factor) = "Select lighting setting (screen use) (choice=Smartphone)"
label(events$lighting_scenario_2___2.factor) = "Select lighting setting (screen use) (choice=Tablet)"
label(events$lighting_scenario_2___3.factor) = "Select lighting setting (screen use) (choice=Computer)"
label(events$lighting_scenario_2___4.factor) = "Select lighting setting (screen use) (choice=Television)"
label(events$autonomy.factor) = "Were the lighting conditions in this setting self-selected (i.e., you had control over lighting intensity, spectrum, or exposure)?"

Next, we condense columns that can be expressed as one. We also lose the .factor extension, as now all doubles are removed. Finally, we simplify entries.

events <- 
events |> 
  rename_with(\(x) x |> str_remove(".factor")) |> #remove .factor extension
  dplyr::mutate(
    type = type |> fct_relabel(\(x) str_remove(x, "-time| time| \\(not wearing light logger\\)")),
    across(c(wear_activity, setting_level01),
           \(x) x |> fct_relabel(\(y) str_remove(y, " \\(.*\\)"))
           ),
    nonwear_activity = 
      nonwear_activity |> fct_recode(
        "Dark mobile" = "Left in a bag, or other mobile dark place",
        "Dark stationary" = "Left in a drawer or cabinet, or other stationary dark place",
        "Stationary" = "Left on a table or other surface with varying light exposure"
      ),
    nighttime = 
      nighttime |> fct_recode(
        "Upward" = "Facing upward on bedside table",
        "Downward" = "Facing downward on bedside table"
      ),
    across(c(setting_level02_indoors, setting_level02_outdoors),
           \(x) x |> fct_recode(
        "Leisure" = "Leisure space (sports, recreation, entertainment)",
        "Commercial" = "Retail, food or services facility",
        "Workplace" = "Working space",
        "Education" = "Learning facility",
        "Healthcare" = "Health facility"
           )
        ),
    setting_level01 = 
      setting_level01 |> fct_recode(
        "Mixed" = "Indoor-outdoor setting"
      ),
    autonomy =
      autonomy |> fct_recode(
        Yes = "Yes, fully self-selected (e.g., adjusting lights at home or in a private office, moving to shaded area)",
        Partly = "Partly self-selected (e.g., some control such as opening blinds or switching a desk lamp, but not over main lighting)",
        No = "Not self-selected (e.g., public transport, shared office, classroom, hospital, airplane, etc.)",
        NULL = "Not applicable"
      )
  ) |> 
  dplyr::rename(setting_light = setting_level01)

events <- 
events |>
  dplyr::mutate(
    scenario_daylight =
      case_when(
        lighting_scenario_daylight___1 == "Checked" ~ "Direct sunlight",
        lighting_scenario_daylight___2 == "Checked" ~ "Shade / cloudy",
        lighting_scenario_daylight___3 == "Checked" ~ "Near a window",
        lighting_scenario_daylight___4 == "Checked" ~ "Away from window"
      ),
    scenario_electric =
      case_when(
        lighting_scenario_3___3 == "Checked" ~ "Darkness",
        lighting_scenario_3___2 == "Checked" ~ "Dim light",
        lighting_scenario_3___1 == "Checked" ~ "Lights on",
      ),
    across(starts_with("lighting_scenario_2___"),
           \(x) ifelse(x == "Checked", TRUE, FALSE)),
  ) |> 
  dplyr::rename(screen_phone = lighting_scenario_2___1,
                screen_tablet = lighting_scenario_2___2,
                screen_pc = lighting_scenario_2___3,
                screen_tv = lighting_scenario_2___4
                ) |> 
  select(-starts_with("lighting_scenario")) |> 
  dplyr::mutate(
    wear_activity = case_when(type == "Wear" ~ wear_activity, .default = NA),
    nonwear_activity = case_when(type == "Non-wear" ~ nonwear_activity, .default = NA),
    nighttime = case_when(type == "Bedtime" ~ nighttime, .default = NA),
    setting_level02_mixed = case_when(setting_light == "Mixed" ~ setting_level02_mixed, .default = NA),
    setting_level02_indoors = case_when(setting_light == "Indoors" ~ setting_level02_indoors, .default = NA),
    setting_level02_outdoors = case_when(setting_light == "Outdoors" ~ setting_level02_outdoors, .default = NA),
    setting_level02_indoors_leisurespace = case_when(setting_level02_indoors == "Leisure" ~ setting_level02_indoors_leisurespace, .default = NA),
    setting_level02_indoors_workingspace = case_when(setting_level02_indoors == "Workplace" ~ setting_level02_indoors_workingspace, .default = NA),
  ) |> 
  unite("type.detail", c(wear_activity, nonwear_activity, nighttime), na.rm = TRUE,
        remove = FALSE) |> 
  unite("setting_location", 
        c(setting_level02_indoors, setting_level02_outdoors, setting_level02_mixed), 
        na.rm = TRUE, remove = FALSE) |> 
  unite("setting_specific", 
        starts_with("setting_level02_indoors_"), 
        na.rm = TRUE, remove = FALSE) |> 
  dplyr::rename_with(\(x) x |> str_remove("_level02")) |> 
  relocate(scenario_daylight, scenario_electric, .before = screen_phone) |> 
  relocate(startdate, .before = 1) |> 
  select(-enddate) |> 
  dplyr::mutate(
    across(c(setting_location, setting_specific),
           \(x) fct_recode(x, NULL = ""))
  )

part_data <- data_devices |> select(-data) |> mutate(record_id = as.character(record_id))

events_complete <- 
events |> 
  dplyr::mutate(record_id = as.character(record_id)) |> 
  left_join(part_data, by = "record_id") |> 
  drop_na(tz_identifier)

label(events_complete$record_id) = "Record ID"

events_complete <- 
events_complete |> 
  dplyr::mutate(
    Datetime = as.POSIXct(startdate, tz = "UTC"),
    UTC_dt = force_tzs(Datetime, tz_identifier),
    .before = 1) |> 
  select(-startdate)

Summaries

In this section we will calculate some summary statistics regarding events

events_complete <- 
events_complete |> 
  dplyr::mutate(status.duration = c(diff(Datetime), na_dbl), 
                .by = record_id,
                .after = Datetime) |> 
  filter(!record_id %in% c("31", "30"))

label(events_complete$status.duration) = "Time between log entries"
label(events_complete$type.detail) = "Wear/Non-wear context"
label(events_complete$setting_location) = "General setting"
label(events_complete$setting_specific) = "Specific indoor setting"
label(events_complete$scenario_daylight) = "Daylight conditions"
label(events_complete$scenario_electric) = "Electric lighting conditions"
label(events_complete$screen_phone) = "Phone use"
label(events_complete$screen_tablet) = "Tablet use"
label(events_complete$screen_pc) = "Computer use"
label(events_complete$screen_tv) = "Television use"
label(events_complete$sex) = "Sex"
label(events_complete$city) = "City"
label(events_complete$country) = "Country"
label(events_complete$latitude) = "Latitude"
label(events_complete$longitude) = "Longitude"
label(events_complete$tz_identifier) = "Time zone identifier"
label(events_complete$UTC_dev) = "Time zone deviation from UTC"
label(events_complete$device_type) = "Used device"
label(events_complete$file_path) = "File path"
label(events_complete$setting_indoors) = "Indoor settings"
label(events_complete$setting_outdoors) = "Outdoor settings"
label(events_complete$setting_mixed) = "Outdoor-Indoor mixed settings"
label(events_complete$wear_activity) = "Activity"
label(events_complete$nonwear_activity) = "Non-wear wearable position"
label(events_complete$nighttime) = "Nightstand wearable measurement direction"

event_summary1 <- 
events_complete |> 
  dplyr::summarize(.by = record_id,
            n = n(),
            mean.duration = mean(status.duration, na.rm = TRUE),
            covered.timespan = last(Datetime) - first(Datetime)
  )
label(event_summary1$n) = "Log entries"
label(event_summary1$mean.duration) = "Mean duration between log entries"
units(event_summary1$mean.duration) = "hours"
label(event_summary1$covered.timespan) = "Total time span of log entries"

event_summary1.tbl <- 
event_summary1 |> 
  tbl_summary(include = -record_id,
              statistic = list(all_continuous() ~ "{mean} ({min}-{max})")
              ) |> 
  modify_caption("**Activity logging (by-participant level)**")

event_summary1.tbl

Activity logging (by-participant level)

Characteristic	N = 481
Log entries	37 (9-80)
Mean duration between log entries	1.71 hours (0.53 hours-3.92 hours)
Total time span of log entries	2.08 days (1.14 days-4.13 days)
1 Mean (Min-Max)

gtsave(event_summary1.tbl |> as_gt(), "tables/table1.png")

file:////var/folders/9p/326_k3kx43qbn_cyl1rqfhb00000gn/T//RtmpGJZj8b/fileb6d66812e4f4.html screenshot completed

event_tbl_setting <- 
events_complete |> 
  select(setting_indoors, setting_outdoors, setting_mixed
         ) |> 
  tbl_summary(missing = "no"
  )

event_tbl_setting

Characteristic	N = 1,7821
Indoor settings
Home	662 (70%)
Workplace	128 (13%)
Education	17 (1.8%)
Commercial	84 (8.9%)
Healthcare	3 (0.3%)
Leisure	23 (2.4%)
Other	32 (3.4%)
Outdoor settings
Home	52 (15%)
Workplace	24 (6.7%)
Education	8 (2.2%)
Commercial	19 (5.3%)
Healthcare	0 (0%)
Leisure	84 (24%)
Other	170 (48%)
Outdoor-Indoor mixed settings
Covered patio or terrace	20 (8.0%)
Semi-open corridor/gallery	11 (4.4%)
Balcony	3 (1.2%)
Veranda	5 (2.0%)
Atrium	1 (0.4%)
Transportation (car/taxi)	139 (55%)
Transportation (bus or commuter/regional rail)	33 (13%)
Transportation (long-distance train)	6 (2.4%)
Transportation (underground, subway)	6 (2.4%)
Transportation (airplane)	7 (2.8%)
Transportation (bike)	5 (2.0%)
Transportation (ferry)	1 (0.4%)
Other	14 (5.6%)
1 n (%)

gtsave(event_tbl_setting |> as_gt(), "tables/table2.png")

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event_tbl_indoors <- 
events_complete |> 
  select(setting_specific
         ) |> 
  tbl_summary(missing = "no"
  )

event_tbl_indoors

Characteristic	N = 1,7821
Specific indoor setting
Bathroom	86 (9.6%)
Bedroom	115 (13%)
Break/lounge area	6 (0.7%)
Classroom	4 (0.4%)
Conference/meeting room	18 (2.0%)
Convenience store/supermarkt	23 (2.6%)
Corridor	10 (1.1%)
Dentist	1 (0.1%)
Drug store	1 (0.1%)
Home office	85 (9.5%)
Kitchen	100 (11%)
Laboratory	2 (0.2%)
Lecture hall	2 (0.2%)
Library	2 (0.2%)
Living room	213 (24%)
Office supply store	1 (0.1%)
Open-plan office area	18 (2.0%)
Other	80 (8.9%)
Parking garage	10 (1.1%)
Personal workspace/desk	72 (8.0%)
Restaurant/cafeteria/bakery	32 (3.6%)
Shopping mall	16 (1.8%)
Studio	1 (0.1%)
1 n (%)

gtsave(event_tbl_indoors |> as_gt(), "tables/table3.png")

file:////var/folders/9p/326_k3kx43qbn_cyl1rqfhb00000gn/T//RtmpGJZj8b/fileb6d6474923fc.html screenshot completed

event_tbl_wear <- 
events_complete |> 
  select(type, wear_activity, nonwear_activity, nighttime
         ) |> 
  tbl_summary(
    missing = "no"
  )

event_tbl_wear

Characteristic	N = 1,7821
Wear type: Are you wearing the light logger at the moment?
Wear	1,553 (88%)
Non-wear	132 (7.4%)
Bedtime	89 (5.0%)
Activity
Sedentary	735 (47%)
Light activity	701 (45%)
Moderate activity	106 (6.8%)
High-intensity activity	11 (0.7%)
Non-wear wearable position
Dark stationary	13 (9.8%)
Dark mobile	22 (17%)
Stationary	88 (67%)
Other	9 (6.8%)
Nightstand wearable measurement direction
Upward	79 (89%)
Downward	5 (5.6%)
Other	5 (5.6%)
1 n (%)

gtsave(event_tbl_wear |> as_gt(), "tables/table4.png")

file:////var/folders/9p/326_k3kx43qbn_cyl1rqfhb00000gn/T//RtmpGJZj8b/fileb6d612d999f4.html screenshot completed

event_tbl_other <- 
events_complete |> 
  select(social_context, setting_light, scenario_daylight, 
         scenario_electric, autonomy
         ) |> 
  tbl_summary(
    missing_text = "Missing"
  )

event_tbl_other

Characteristic	N = 1,7821
Are you alone or with others?
Alone	1,039 (59%)
With others	736 (41%)
Missing	7
Select the setting
Indoors	949 (61%)
Outdoors	357 (23%)
Mixed	251 (16%)
Missing	225
Daylight conditions
Away from window	484 (29%)
Direct sunlight	224 (14%)
Near a window	727 (44%)
Shade / cloudy	223 (13%)
Missing	124
Electric lighting conditions
Darkness	357 (28%)
Dim light	243 (19%)
Lights on	668 (53%)
Missing	514
autonomy
Yes	828 (58%)
Partly	176 (12%)
No	433 (30%)
Missing	345
1 n (%)

gtsave(event_tbl_other |> as_gt(), "tables/table4.png")

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event_tbl_duration <- 
events_complete |> 
  drop_na(setting_light) |> 
  dplyr::summarize(`Daily duration` = sum(status.duration, na.rm = TRUE),
                   .by = c(setting_light, record_id)) |> 
  dplyr::summarize(`Daily duration` = mean(`Daily duration`),
                   .by = c(setting_light)) |> 
  dplyr::mutate(`Daily duration` =
                  `Daily duration` /
                  sum(as.numeric(`Daily duration`)) *
                  24*60*60,
                Percent = 
                  (as.numeric(`Daily duration`)/
                  sum(as.numeric(`Daily duration`)))
                  # vec_fmt_percent()
                ) |> 
  gt(rowname_col = "setting_light") |> 
  grand_summary_rows( 
                     fns = list(
      sum ~ sum(.)
    ),
    fmt = list(~ fmt_percent(., columns = Percent), 
               ~ fmt_duration(., columns = `Daily duration`, 
               input_units = "secs",
               max_output_units = 2))
    ) |> 
  fmt_duration(`Daily duration`, 
               input_units = "secs",
               max_output_units = 2) |> 
  fmt_percent(columns = Percent) |> 
  tab_header(title = "Mean daily duration in condition")

event_tbl_duration

Mean daily duration in condition
	Daily duration	Percent
Outdoors	2h 38m	11.02%
Mixed	3h 42m	15.42%
Indoors	17h 39m	73.56%
sum	1d	100.00%

gtsave(event_tbl_duration, "tables/table5.png")

file:////var/folders/9p/326_k3kx43qbn_cyl1rqfhb00000gn/T//RtmpGJZj8b/fileb6d67513f548.html screenshot completed